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PLoS One. 2014 Nov 11;9(11):e111048. doi: 10.1371/journal.pone.0111048. eCollection 2014.

Connectotyping: model based fingerprinting of the functional connectome.

Author information

1
Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, Oregon, United States of America.
2
Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, Oregon, United States of America; Division of Neuroscience, Oregon National Primate Research Center, Beaverton, Oregon, United States of America.
3
Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, Oregon, United States of America; Advanced Imaging Research Center, Oregon Health and Science University, Portland, Oregon, United States of America; Division of Neuroscience, Oregon National Primate Research Center, Beaverton, Oregon, United States of America.
4
Department of Psychiatry, Oregon Health & Science University, Portland, Oregon, United States of America; Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, Oregon, United States of America.
5
Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, Oregon, United States of America; Advanced Imaging Research Center, Oregon Health and Science University, Portland, Oregon, United States of America; Department of Psychiatry, Oregon Health & Science University, Portland, Oregon, United States of America.

Abstract

A better characterization of how an individual's brain is functionally organized will likely bring dramatic advances to many fields of study. Here we show a model-based approach toward characterizing resting state functional connectivity MRI (rs-fcMRI) that is capable of identifying a so-called "connectotype", or functional fingerprint in individual participants. The approach rests on a simple linear model that proposes the activity of a given brain region can be described by the weighted sum of its functional neighboring regions. The resulting coefficients correspond to a personalized model-based connectivity matrix that is capable of predicting the timeseries of each subject. Importantly, the model itself is subject specific and has the ability to predict an individual at a later date using a limited number of non-sequential frames. While we show that there is a significant amount of shared variance between models across subjects, the model's ability to discriminate an individual is driven by unique connections in higher order control regions in frontal and parietal cortices. Furthermore, we show that the connectotype is present in non-human primates as well, highlighting the translational potential of the approach.

PMID:
25386919
PMCID:
PMC4227655
DOI:
10.1371/journal.pone.0111048
[Indexed for MEDLINE]
Free PMC Article

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